Estimating the location of a mobile unit based on the elimination of improbable locations

ABSTRACT

A method for determining the location of a mobile unit (mobile unit) in a wireless communication system is disclosed. The illustrative embodiment provides a computationally-efficient technique for reducing the number of possible positions that have to be analyzed. In particular, the illustrative embodiment eliminates possible positions for the mobile unit from consideration by considering which signals the mobile unit can—and cannot—receive and the knowledge of where those signals can and cannot be received.

BACKGROUND OF THE INVENTION

The present invention relates generally to telecommunications, and morespecifically to wireless communication systems.

In connection with mobile communication systems, it is becomingincreasingly important to determine the location of the communicatingMobile Unit. Various systems for locating are already well known. Onesolution that is readily available in most modern cellular systems is touse the ID of the cell from which the mobile unit is communicating.Typically, this information is accurate to a resolution of severalmiles. A second solution is to compute the location of the mobile unitbased on the cellular network signaling parameters (angle of arrival ortime difference of arrival). This information is typically accurate tohundreds of meters. Yet another solution is to equip the mobile unitwith a GPS receiver which then attempts to track the location of themobile unit as accurately as possible. Typically, GPS receivers cancompute locations to within several tens of meters of accuracy. Whencombined with differential corrections, the GPS accuracy can beimproved.

As far as reliability is concerned, the cell ID information is the mostreliable, and is guaranteed to be available as long as the cellularnetwork is functioning normally. The network signal based locationcomputations are less reliable, since they are dependent on severalconditions being true at the time of the call. For example, most schemesrequire the mobile unit to have line-of-sight visibility to multiplecellular base stations. This is not always possible. GPS based locationcomputation is also not always reliable since the mobile unit may be inan environment where there is no penetration of the GPS satellitesignals.

SUMMARY OF THE INVENTION

The present invention provides a method for determining the location ofa mobile unit without some of the costs and disadvantages associatedwith techniques in the prior art. In the prior art, a very large numberof possible positions might have to be analyzed to determine if themobile unit is there and this can be computationally cumbersome.Therefore, any computationally-efficient technique that reduces thenumber of possible positions that have to be analyzed is advantageous.The illustrative embodiment provides a computationally-efficienttechnique for reducing the number of possible positions that have to beanalyzed. In particular, the illustrative embodiment uses three logicaltests to reduce the number of possible locations whose fingerprints mustbe considered. The three tests are:

-   -   Test #1—Eliminate all locations outside the serving area of the        cell that is reported as the serving cell.    -   Test #2—Eliminate all locations outside the neighbor area of        each of the cells that is reported as a neighbor cell.    -   Test #3—Eliminate all locations at which an unreported neighbor        is known to be significantly stronger than a reported neighbor.

Some embodiments of the present invention also improve location accuracyby eliminating potentially ambiguous positions from consideration.

The illustrative embodiment comprises: a method for estimating thelocation of a mobile unit from a plurality of possible positions, themethod comprising: receiving an indication from the mobile unit that themobile unit does not receive a first signal from a first transmitter;eliminating some of the plurality of possible positions fromconsideration based on the fact that the mobile unit did not receive thefirst signal from the first transmitter; and choosing as an estimate ofthe position of the mobile unit one of the plurality of possiblepositions not eliminated from consideration based on the fact that themobile unit did not receive the first signal from the first transmitter.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a representative wireless communication system;

FIG. 2 is a high level diagram of the Mobile Unit;

FIG. 3 is a flow diagram of the position determining process employed bythis invention;

FIG. 4 is a block diagram showing the technique for implementingcorrections for co-channel and adjacent-channel interference;

FIG. 5 is a block diagram describing the technique for calibrating thepredicted fingerprint database using field measurements;

FIG. 6 is a block diagram showing the operation of the recursiveprocessing Markov algorithm;

FIG. 7 describes the Markov transition probabilities;

FIG. 8 is a block diagram showing the operation of the multiplehypothesis testing algorithm;

FIG. 9 is an illustration of the organization of the fingerprint data;and

FIG. 10 is an illustration of the organization of the fingerprintdatabase.

FIG. 11 depicts a map of a geographic region that is serviced by atypical wireless telecommunications system.

FIG. 12 depicts a flowchart of a technique in accordance with theillustrative embodiment.

FIG. 13 depicts a map of the serving area and the neighbor areaassociated with a cell site.

FIG. 14 depicts a map of the serving areas and the neighbor areasassociated with Cell Site A, Cell Site B, and Cell Site C.

FIG. 15 depicts a map of the locations that can be eliminated becausethey are outside the serving cell's serving area.

FIG. 16 depicts a map of the locations that can be eliminated becausethey are outside the reported neighbor cell's neighbor area.

FIG. 17 depicts a map of the locations that can be eliminated because anunreported signal is known to be stronger than one of the reportedsignals.

FIG. 18 depicts a map of all of the locations that can be eliminated inaccordance with FIGS. 15, 16, and 17.

FIG. 19 depicts a graph that illustrates the logic that underlies howthe illustrative embodiment eliminates some locations from considerationgiven the relative signal strength of two signals.

DESCRIPTION OF SPECIFIC EMBODIMENTS

The present invention provides a new method for determining the locationestimate of a Mobile Unit (mobile unit) in a wireless communicationnetwork.

FIG. 1 is a high level block diagram of a wireless communicationnetwork. A Mobile Unit 10 has a connection with a wireless network 15,which in turn is connected to location server 30. The location servermay or may not be mobile. The location of the mobile unit is of interestto the location server for several reasons such as provisioning ofprompt and efficient personalized services, dispatching emergencyassistance personnel, tracking the movements of the mobile unit, etc.

There are several different prior art methods for determining thelocation of mobile unit 10, as is known to one skilled in the art. Forexample, the mobile unit could be equipped with a GPS receiver.Alternatively, the wireless network could be equipped to determine thelocation of mobile unit 10. For example, the network could monitor thetime of arrival of signals from the mobile unit at various nodes andfrom that information determine its location. Again, such techniques arewell known to one skilled in the art.

All of the prior art techniques have significant disadvantages. Forexample, it is well known that GPS receivers do not work very well inurban canyons and indoor locations where signal strength is very low.The network based schemes such as TDOA and AOA (both well known priorart) are disadvantaged in that they need significant infrastructuremodifications.

The present invention provides a new method for determining the locationof mobile unit 10 which (a) works in areas where GPS coverage is nottypically available, and (b) does not require any infrastructuremodifications. Thus, the present invention complements existing locationdetermining technologies and, when used in conjunction with them,augments their performance.

The invention is based on the principle that any location has a uniqueRadio Frequency (RF) spectral fingerprint. Spectral fingerprint in thiscontext is defined as a predetermined combination of observable RFspectral parameters. For instance, observed signal strength of apredetermined set of signals in the RF spectrum constitutes afingerprint. Today, worldwide, practically the entire RF spectrum, up to2 GHz and above, is being utilized by several different applications.The signal characteristics vary greatly across this spectrum. However,for any given location, it is possible to pre-select a portion of thespectrum and a combination of signal parameters in the pre-selected bandthat will be unique to that location.

In accordance with the invention mobile unit 10 is equipped withcircuitry and software that is capable of capturing information frompredetermined portions of the RF spectrum. In one embodiment thepredetermined portions of the RF spectrum all fall within or in closeproximity to the same band as that utilized by the wirelesscommunication network. In such an instance the same hardware circuitrycan be used for performing both functions. In another embodiment thepredetermined portions of the RF spectrum are different from thewireless communication band and in such an instance additional circuitryis required. For example, the mobile unit may use signal characteristicsfrom the television UHF band, in which case it will require a televisiontuner capable of capturing the appropriate television channels. Inanother example the mobile unit is equipped with a tuner designed tocapture AM or FM radio broadcasts. In this case the mobile unit isequipped with a radio capable of tuning to the appropriate radiobroadcasting bands.

FIG. 2 shows the mobile unit containing a component 101 for tuning to apredetermined portion of the RF spectrum. Also included is acommunication component 105 for communicating information with thelocation server over an existing wireless infrastructure. Component 101obtains information from the RF spectrum via an Antenna 102. In oneembodiment of the system, the communication link between the mobile unitand location server is through the base stations and base stationcontrollers of the cellular network. In another embodiment of thesystem, data is communicated in both directions between the mobile unitand location server by using the Short Messaging System (SMS) of thenetwork. Using SMS messages for implementation of this invention has theadvantage of avoiding potential interference with voice channel networkoperations.

In many instances, location server 30 is interested in only determiningif mobile unit 10 is at a particular location or not. The resolution ofknowing the mobile unit's location is not high (e.g., several meters),but much coarser, such as of the order of several tens of meters. Forexample, location server 30 may be interested in knowing if mobile unit10 is inside a particular building, or a campus or a block. In suchcases it is not necessary to provide very high-resolution information tolocation server 30.

There are other instances where location server 30 is desirous ofknowing the accurate location of mobile unit 10, however, is incapableof doing so. This could be because other location determiningcapabilities in the system, such as GPS, are not functional at theinstant when the location information is desired. This is typical whenthe mobile unit is in an area where GPS signals are not available, suchas inside a building. The location determining method described in thisinvention is capable of operating in areas where GPS and other locationtechnologies are not.

When a location estimate of the mobile unit is desired (either by themobile unit itself or by the location server), it activates component101 (FIG. 2), which captures predetermined information from apredetermined portion of the RF spectrum. Instructions regarding whatinformation to capture and the portion of the RF spectrum from which tocapture may be either pre-programmed in the mobile unit, or generated inreal time. In the latter case, it may be generated in the mobile unit,or downloaded into the mobile unit from the location server over thewireless network. The mobile unit may capture multiple pieces ofinformation or from multiple portions of the spectrum.

The spectral fingerprint may be generated using many differentparameters, either individually or in combination. In one embodiment,signal strength is used. In another embodiment, phase information isused. In another embodiment, the identity of the received signals (e.g.,frequency) is used. In yet another embodiment, the identity of thesignal source (e.g., channel number or station code) is used. In yetanother embodiment, the geographic locations of the transmitters fromwhich the signals originate are used.

A mobile cellular channel is in general frequency selective, i.e., itsproperties vary over the bandwidth used. The variation depends on theenvironment, because of multipath signals arriving at different delays.For a GSM signal with a bandwidth about 250 kHz, the dispersion can befelt in urban and hilly environments. It causesinter-symbol-interference, which results in digital errors unlesscorrected. The time dispersion is often expressed as the delay spread orthe standard deviation of the impulse response. The remedy in a receiveris to have an equalizer, typically a transversal filter with some tapsthat can be adjusted. This filter will remove theinter-symbol-interference. The filter's tap values depend on theenvironment and impulse response, and thus in principle add additionalinformation to the RF fingerprint. For future systems which use W-CDMA(wideband code division multiple access), the bandwidth is more than 10times higher, of the order 4-5 MHz. The resolution power is thus muchhigher, and the various filter taps contain much more information. Inthis embodiment of the invention, the spectral characteristics withinthe used bandwidth (and not just individual center frequencies), asmeasured by the equalizer filter, will be included in the RFfingerprint.

The mobile unit is equipped with the appropriate circuitry and softwareto capture the required signals and their parameters. In one embodimentthe mobile unit has an antenna that is designed to have a bandwidthspanning a large portion of the VHF and UHF spectrum, e.g., from 70 MHzto 1 GHz. In another embodiment, the mobile unit has an antenna that isdesigned to capture only a narrowband of the spectrum. Such an antennamay be cheaper to implement and unobtrusive. In one embodiment themobile unit is equipped with appropriate circuitry to determine thestrength of the received signal. In one instance the location of thetransmitter is broadcast in the signal and is extracted in the mobileunit.

In one embodiment, the mobile unit is instructed by the location serverto scan selected portions of the spectrum and capture selectedparameters from the received signals. The location server determineswhich portions of the spectrum to scan and what parameters to capturebased on other information it has received or generated regarding themobile unit. For example, in one instance the location server knows theapproximate location of the mobile unit by receiving identity of the(wireless communication network) cell that the mobile unit is in at thattime. By looking up a database the location server can determine thegeographic location of the cell. The location server then determineswhich signals in the vicinity of the cell are most suitable forgenerating a fingerprint. For example, certain television signals mayhave better coverage of the cell than other signals. The cellularnetwork then transmits this information (e.g., television channelnumbers) to the mobile unit via the wireless link requesting it to scanonly those selected signals.

In another embodiment, the mobile unit determines which portion of thespectrum to scan, and what parameters to use for generating thefingerprint.

After the mobile unit captures the appropriate signals and extracts theparameters, it has the basic information for generating the fingerprint.Some preprocessing may be required to refine the raw data. For example,signal strengths may have to be lower and upper limited to eliminatevery weak and very strong signals.

Once the fingerprint is generated, its association with a certainlocation has to be determined. According to this invention this is doneby utilizing a fingerprint database that contains a number offingerprints along with their corresponding location identities. In oneembodiment the database is stored in the mobile unit. The generatedfingerprint is compared with the fingerprints in the database and thefingerprint in the database that is closest to the generated fingerprintis selected as the match. The corresponding location in the database isthen chosen as the location of the mobile unit. In one embodiment, thesearch algorithm takes more than one fingerprint from the database thatare closest to the generated fingerprint and interpolates the mostplausible location for the mobile unit from the locations of the chosenfingerprints.

In another embodiment, the fingerprint database is stored at thelocation server and the generated fingerprint (in the mobile unit) istransmitted to the location server over the wireless link. The searchfor the closest fingerprint is then done in the location server fromwhich it determines the location of the mobile unit.

FIG. 3 depicts the flow of events in this case. A request for positionof the mobile unit is generated, as shown in box 301. The request may begenerated by the user carrying the mobile unit, or remotely by thelocation server. On receipt of the request the mobile unit captures thefingerprint of its current location (box 303). The captured fingerprintis processed appropriately. Processing may include filtering thefingerprint data and reformatting it to reduce its storage space.Subsequently the fingerprint is transmitted over the wireless link tothe location server as shown in box 305. The location server has adatabase into which it executes a search for the closest matchingfingerprint, as shown in box 307. Box 309 shows the process culminatingin the retrieval of the best matching fingerprint along with itscorresponding location. In one embodiment the search also returns aconfidence measure that depicts the closeness of the match.

According to one implementation, the fingerprint database is designed tocompensate for any co-channel and adjacent-channel interference measuredby the mobile unit (mobile unit). This channel interference can resultfrom either control channels or traffic channels. To improve theaccuracy, as well as viability, of any algorithm for mobile unitlocation based on measurements of base station signal strengths,corrections for channel interference will be required. Theseinterference corrections are calculated and updated off-line using RFprediction models (the same models employed to generate the originalfingerprint database) and the wireless carrier's frequency/channelallocation plan (FCAP). In one embodiment of the invention, theinterference corrections are implemented in the fingerprint database,which is stored in the location server.

FIG. 4 describes the data flow and processing requirements for thistechnique. The data required from the wireless carrier's FCAP is asfollows: cell global identifiers, frequency identifiers for the assignedbase station control channels, and frequency identifiers for theassigned traffic channels. Co-channel interference sources areidentified using the FCAP to identify base stations that are assignedthe same frequency channel; the corrected signal strengths in eachchannel are then calculated. Adjacent-channel interference sources areidentified using the FCAP to identify base station pairs that areassigned adjacent frequency channels; the corrected signal strengths arethen calculated by also using the mobile unit's filter discriminationratio. The resulting interference-corrected fingerprints are stored inthe fingerprint database.

According to one implementation, the fingerprint database is generatedby computer calculations with RF prediction models using certainparameters which depend on the type of buildings, terrain features, timeof the day, environmental and seasonal conditions, etc. The calculationsare based upon theoretical models which have general applicability, butcannot consider all of the details in the real world. The accuracy ofthe fingerprint database can be enhanced by comparing the modelpredictions with field measurements as shown in FIG. 5. The adaptationalgorithm analyzes the nature of the difference between the predictedand measured fingerprints and determines the best way of changing themodel parameters to reduce the difference. Since the database consistsof multiple frequencies, the difference is a multi-dimensional vector.The minimization of the difference can use a combination of advancedcomputational techniques based upon optimization, statistics,computational intelligence, learning, etc.

The adaptation algorithm also determines what measurements to collect tobest calibrate the RF prediction model. Instead of collecting allmeasurements, only measurements that can provide the maximal informationto support reduction of the prediction errors are collected. Thespecific choice of measurements to collect is based upon an analysis ofthe nature of the error and the desired accuracy of the predictionmodel. The algorithm determines not just the locations for measurementcollection, but also the time of collection, and the number ofmeasurement samples needed to achieve a certain degree of accuracy basedupon statistical considerations.

To improve the accuracy of the fingerprint database, learning/trainingtechniques and methodologies can be adopted for this problem. For thisembodiment of the invention, either Support Vector Machines forRegression or Regularized Regression can be used to learn each mappingof position versus fingerprint (measured or predicted signal strengths).Position will be used as the input value, while signal strength will beused as the target value. The complete grid of locations for thecellular service area will be used as training data. The final result isa learned relationship between position and signal strength for eachmapping so that, given an arbitrary position, the corresponding signalstrength is obtained for each particular frequency channel.

The inputs of the training step are a set of positions matched with thecorresponding signal strengths, and the fingerprint measurements whoseinformation should be considered in the learning process are added tothe training set. The machine is then re-trained and a new grid isgenerated for the database, to replace the old one. The following listsummarizes the major advantages of learning/training techniques: (1)they offer a way to interpolate between grid points and reconstruct thegrid, together with metrics to relate measurements and expectedfingerprints; (2) a local improvement of the neighboring area resultsfrom adding one measurement to the training set, and not only theparticular location is affected, therefore, there is no need to obtainmeasurements for the complete grid; (3) measurements taken at differenttimes can help to produce specific databases for different times of theday, different weather conditions, or different seasons; and (4) thesystem can learn which are the ambiguous areas with an intelligentagent, and collect measurements for them, so the database can becontinually improved.

According to one implementation, the fingerprint database is designed totake into account any dynamic, but predetermined, variations in the RFsignal characteristics. For example, it is not uncommon that some AMradio broadcast stations lower their transmitter power at night tominimize interference with other stations. In some countries this ismandated by law. If signal strength is one of the parameters used forgenerating the fingerprint, then it is essential that the dynamic changein transmitted power be taken into consideration before any decision ismade. According to this aspect of the invention, the fingerprintdatabase and the decision algorithms are designed to accommodate suchdynamic changes. Since the change pattern in signal characteristics ispredetermined, the database is constructed by capturing the fingerprintsat different times so as to cover all the different patterns in thetransmitted signals. The time at which a fingerprint was captured isalso stored along with its location identity.

There are many choices for the search algorithm that is required todetermine the closest matching fingerprint, as can be appreciated by oneskilled in the art of statistical pattern matching. Specifically, thechoice of the algorithm is a function of what parameters are used togenerate the fingerprints. In one instance the search algorithm choosesthe fingerprint from the database that has the smallest mathematicaldistance between itself and the captured fingerprint. The mathematicaldistance is defined as a norm between the two data sets. For example, itcould be the average squared difference between the two fingerprints.There are many different ways to define “closeness” between twofingerprints; again, this is dependent on the signal parameters used togenerate the fingerprints. In one embodiment the search algorithm alsohas built in heuristics that make the best possible decision in casenone of the fingerprints in the database matches well with the generatedfingerprint.

Mobile unit (mobile unit) location determination that separatelyprocesses single time samples of the first fingerprints (i.e., measuredfingerprints) will herein be referred to as “static location,” which hasbeen shown to be an inherently inaccurate solution. Using a processingapproach based on the time series nature of the measured fingerprints,herein referred to as “dynamic location,” can yield significantimprovements in location accuracy with respect to static locationtechniques. In this embodiment of the invention, a dynamic locationtechnique, which is based on Markov theory and exploits the time historyof the measured fingerprints, is described in FIGS. 6 and 7. Theprocedure uses a predicted fingerprint model and a time series offingerprint measurements from the mobile unit to calculate theprobability distribution of the mobile unit's location. The probabilitydistribution is then used to compute an estimate of the current locationof the mobile unit using one of several statistical methods.

As shown in FIG. 6, the processing algorithm is recursive and uses apredefined array of possible locations. When the algorithm has processedthe time series of fingerprint measurements currently available, it willhave calculated the probability that the mobile unit is at each point inthe array of possible locations (the updated probability distribution).This probability distribution is the basic state of the algorithm thatincorporates all of the information that has been collected on themobile unit up to the current time. When a new fingerprint measurementbecomes available, the algorithm uses its knowledge of the possiblemotions of the mobile unit during the time period between the lastmeasurement and the new measurement to predict a new locationprobability distribution (the predicted distribution). This predictionalgorithm is based on a Markov process model, which uses the probabilitythat the unit is at each location and the probability that it willtransition from one location to another to calculate the probabilitythat it will be at each location at the next measurement time (see FIG.7). Next, the algorithm uses the predicted signature model and the newsignature measurement to calculate a location probability distributionbased on the new measurement alone. This probability distribution isthen combined with the predicted distribution to produce a new updateddistribution that now incorporates the information provided by the newmeasurement.

Before any measurements are available, the algorithm starts with aninitial probability distribution that assumes that the mobile unit isequally likely to be at any location in the cell for the base stationhandling the call. The location probability distribution (either updatedor predicted) may be used to compute an estimate of the mobile unit'slocation whenever such an estimate is needed. The basic algorithmcalculates both the expected location and the most probable location,but other location statistics (as deemed appropriate) could also becalculated from the location probability distribution. The locationestimate may be calculated for either the time of the last fingerprintmeasurement (which yields a filtered estimate) or for some time beforethe last fingerprint measurement (which yields a smoothed estimate). Thesmoothed estimate is more accurate than the filtered estimate, becauseit uses more information (i.e., some number of subsequent measurements).However, the smoothed estimate requires both a larger state and morecomputation than the filtered estimate.

Matching the measured fingerprints to the fingerprint database may yieldmultiple location possibilities in the presence of measurement andmodeling errors. A single hypothesis approach that selects the bestlocation estimate for the mobile unit using only the current measuredfingerprint is likely to produce erroneous results. In this embodiment,a multiple hypothesis testing technique uses measured fingerprints frommultiple times, the fingerprint database, and mobile unit motionconstraints to generate multiple hypotheses for mobile unit locationsover time and then selects the best hypothesis based upon statisticalmeasures.

In FIG. 8, the measured fingerprints are compared with the fingerprintdatabase to generate possible mobile unit location hypotheses. Whenprevious location hypotheses are available, these hypotheses are updatedwith the latest measurements to form new hypotheses. The number ofhypotheses is reduced by considering only those locations that areconsistent with the previous location hypotheses. A previous locationhypothesis may yield multiple new hypotheses because of possible newlocations. However, some previous location hypotheses may not haveconsistent new locations based on the new measurements. A hypothesisevaluation algorithm then assigns a score to each of the updatedlocation hypotheses. This score measures how well the locations atdifferent times in the hypothesis are consistent with reasonable mobileunit motion and measurements characteristics. The evaluation of thisscore is based upon statistical techniques and uses mobile unit motionconstraints, the fingerprint database, and the measured fingerprintcharacteristics. The score also depends on the score of the previoushypothesis. The location hypotheses are then ranked according to thisscore. A hypothesis management algorithm reduces the number ofhypotheses by removing hypotheses with low scores and combining similarhypotheses. The surviving hypotheses are then updated with newmeasurements.

If the search takes too long to complete, the resulting answer will bestale and ordinarily of little value. Therefore, the illustrativeembodiment seeks to shorten the time that the search takes to complete.

The length of time that the search takes to complete is related to itscomputational complexity. Furthermore, the computational complexity ofthe search is related to the number of fingerprints that must beconsidered, and the number of fingerprints that must be considered isrelated to the number of possible locations where the mobile unit couldbe. Therefore, the length of time that the search takes to complete isrelated to the number of possible locations of the mobile unit that mustbe considered.

As the number of possible locations to search increases, the length oftime to complete the search also increases. Analogously, as the numberof possible locations to search decreases, the length of time tocomplete the search also decreases. Therefore, the illustrativeembodiment seeks to reduce the length of time it takes to complete thesearch by reducing the number of possible locations that must besearched.

The number of possible locations of the mobile unit that must besearched can be reduced when an approximate estimate of the mobileunit's location is available. For example, an approximate estimate ofthe mobile unit's location can be determined from the identity of thecell site serving the mobile unit. This can, for example, reduce thenumber of possible locations by a factor of one thousand or more.

Similarly, the number of possible locations that must be searched can bereduced by knowing which signals the mobile unit has reported it is ableto receive and distinguish. This concept is described in detail belowand with regard to FIGS. 11 through 19.

FIG. 11 depicts a map of a geographic region that is serviced by awireless telecommunications system. In this wireless telecommunicationssystem, there are three omnidirectional cell sites, Cell Site A, CellSite B, and Cell Site C, that are responsible for providing service tothe mobile units (e.g., mobile unit 1101) within the purview of thesystem.

At any given moment, each mobile unit is at one of a number oflocations, and the flowchart depicted in FIG. 12 describes a method forestimating the location of the mobile unit so that the number offingerprints to be searched can be reduced.

Initially, the location server maintains a database that lists all ofthe possible locations for the mobile unit. Table 1 depicts a portion ofsuch an illustrative database. The database correlates to each location:

i. the fingerprint associated with that location,

ii. the identities of the cell sites that could serve that location,

iii. the identities of the cell sites that could be received asneighbors at that location.

How this information is used will be described in detail below. Forexample, the fingerprint is the signal, so it doesn't need to be savedagain. Furthermore, in the illustrative embodiment the absolute signalstrength is saved and not the relative signal strength because thecalculation of relative signal strength must be done in real-timebecause it depends on which signals are reported (i.e., signal strengthof one signal relative to another reported signal) and there are toomany possible combinations of reported and unreported signals. It willbe clear to those skilled in the art how to compile and maintain such adatabase.

TABLE 1 Database of Possible Locations of Mobile Unit Possible PossibleFinger- Serving Neighbor Location Latitude Longitude print Cell Site(s)Cell Site(s)    1 47° 10′ 127° 23′ F₁ A, B B, A 23.12″ N. 41.73″ W. . .. . . . . . . . . . . . . . . . 60,000 47° 13′ 127° 26′ F_(60,000) C A,C 46.33″ N. 44.53″ W.

In accordance with the illustrative example, and as shown in Table 1,the mobile unit is at one of sixty thousand (60,000) possible locations.It will be clear to those skilled in the art, however, after readingthis specification, that other systems might comprise a different numberof locations. Although the location server could compare the RFfingerprints received from the mobile unit to all 60,000 possiblelocations, the method depicted in FIG. 12 eliminates some of the 60,000possible locations based on which signals the mobile unit is able toreceive and the relative strength of those signals in every location.

FIG. 12 depicts a flowchart of the tasks associated with reducing thenumber of possible locations that must be searched in accordance withthe illustrative embodiment. Tasks 1201 though 1203 are normallyperformed by the cellular network as a part of its operation. Task 1207is the operation of the base location server described in thisspecification. Tasks 1204 through 1206 are elements of the presentinvention, and their purpose is to reduce the number of possiblepositions that must be considered by task 1207.

At task 1201, the cellular network determines one or more signals thatthe mobile unit might or might not be able to receive. To accomplishthis, the cellular network comprises a database that lists which signalsmight be receivable by the mobile unit.

In accordance with the illustrative embodiment, each receivable signalis a base station control channel, which can be distinguished based on aBase Station Identity Code. It will be clear to those skilled in the arthow to make and use embodiments of the present invention that are keyedto a different signal.

At task 1202, the cellular network directs the mobile unit to attempt toreceive the signals in Table 2 and to and report back a signal strengthvalue for the strongest (up to) M signals that the mobile unit is ableto receive and distinguish, wherein M is a positive integer. Forexample, when M=3 and the mobile unit is able to receive and distinguish4 signals, the mobile only reports back the signal strength for the 3strongest. On the other hand, when M=3 and the mobile unit is able toreceive and distinguish 2 signals, the mobile only reports back thesignal strength for the 2 signals.

In accordance with the illustrative embodiment, consider the situationwhere Cell A is the serving cell (i.e., the cell currently handling thecommunications traffic to and from the mobile) and the cellular systemdirects the mobile to monitor signals on control frequencies of Cell Band Cell C and to report the strongest of these two neighbors that itcan distinguish. Note that the mobile can obviously distinguish thesignal from Cell A; otherwise Cell A could not be acting as the servingcell.

At task 1203, the cellular network receives the signal strength reportfrom the mobile unit in response to the direction in task 1202. Withthis information and the data in Table 1, the location server uses threelogical tests to reduce the number of possible locations whosefingerprints must be considered. The three tests are:

-   -   Test #1—Eliminate all locations outside the serving area of the        cell that is reported as the serving cell.    -   Test #2—Eliminate all locations outside the neighbor area of the        cells that are reported as neighbor cells.    -   Test #3—Eliminate all locations at which an unreported neighbor        is known to be stronger than a reported neighbor.        In accordance with the illustrative embodiment, the location        server receives a report on 2 signals, Signal A=−11 dBm, which        is identified as being associated with the serving cell, and        Signal B=−14 dBm.

As shown in FIG. 13, there is an area called the “serving area” aroundeach cell site where that cell site could serve a mobile unit.Furthermore, there is a larger area called the “neighbor area” aroundeach cell site where that cell site could be recognized by a mobile unitas a neighbor. In the illustrative embodiment, both the serving area andthe neighbor areas are depicted as circles with the cell site at theircenter. It will be clear to those skilled in the art how to make and useembodiments of the present invention in which the serving areas andneighbor areas have different shapes and sizes.

FIG. 14 depicts a map of wireless telecommunications system 1100, andthe respective serving and neighbor areas for each of the three cellsites. From FIG. 14, it should be noted that some locations can beserviced by all three cell sites, some by only two cell sites, and someby only one cell site. Furthermore, some locations are neighbors fornone, one, or two cell sites.

At task 1204, in accordance with Test #1, the location server eliminatesfrom consideration all locations outside of the serving cell's servingarea.

Because Cell A is the serving cell, the location server eliminates fromconsideration all locations outside of Cell Site A's serving area. Thisis depicted graphically in FIG. 15, and can be computed from the list ofpossible serving sites in Table 1.

At task 1205, in accordance with Test #2, the location server eliminatesfrom consideration all locations outside of the reported neighbor cell'sneighbor area.

In the illustrative embodiment, Signal B, which is associated with CellSite B, was reported as a neighbor signal at task 1203. Therefore, asgraphically depicted in FIG. 16, all locations outside of the neighborarea for Cell Site B are eliminated from consideration. This can becomputed from the list of possible neighbor sites in Table 1.

At task 1206, in accordance with Test #3, the location server eliminatesfrom consideration all locations that would have been reported in task1203 had the mobile unit been, in fact, at that location. For example,in the illustrative embodiment the mobile unit was directed to reportthe stronger or two signals from neighbors B and C and the mobile unitreported Signal B. Therefore, the location server can logicallyeliminate from consideration all locations where Signal C is known to bestronger than Signal B. This can be computed from the fourth column ofTable 1.

Because neighbor Signal B was reported in task 1203 and neighbor SignalC was not, FIG. 17 depicts a map of the locations that can be eliminatedbecause Signal C is stronger than Signal A at those locations.

In summary, FIG. 18 depicts a map of the locations that can beeliminated through Test #1, #2, or #3, and, therefore, it also depictsthe locations whose fingerprints must be searched.

Because there is a range of uncertainty in the measurement of allsignals, the boundary of which locations can—and which cannot—mustconsider that range of uncertainty. FIG. 19 depicts a graph thatillustrates the logic that underlies which locations can be eliminatedbased on the range of uncertainty in the measurement of the signals.Taking this effect into account, the dividing line in FIG. 17 istechnically closer to cell site C than it is to cell site B, rather thanequidistant between the two.

At task 1207, the location server chooses as an estimate of the locationof the mobile unit one of the possible locations not eliminated fromconsideration in tasks 1204, 1205, or 1206, and as depicted in FIG. 18.Furthermore, the search complexity is reduced by noting the time atwhich the location information is requested.

FIG. 9 illustrates a structure 400 of the fingerprint in one embodiment.As mentioned previously there are several possible methods for definingthe fingerprint; FIG. 9 is but an example. The time at which thefingerprint is captured is stored in the fingerprint structure, as shownby box 401. In one embodiment the UTC format is used to store time.There are several fields in the structure, some of which are optionallyfilled by the mobile unit. Some other fields are optionally filled bythe location server. It is not necessary that all fields be filled,since the necessary fields can be predetermined based on systemparameters.

The fingerprint comprises characteristics of received signals atmultiple frequencies. Each column in FIG. 9 is information pertaining toa particular frequency or carrier. A Station ID field 403 indicates theunique identifying code of a broadcasting station from which the signalemanated. This field is optional. In one embodiment this field is filledby the mobile unit using information received in the signal. In anotherembodiment this field is filled by the location server to indicate tothe mobile unit as to which signals to capture for the fingerprint. AFrequency field 405 is the unique frequency value at which a signal iscaptured. Either the Station ID field or the Frequency field ismandatory since without both it is not possible to identify the signal.A Tuning Parameter field 407 is used when the mobile unit requiresadditional information to tune to a particular carrier. In oneembodiment this field is supplied by the location server withinformation containing the modulation characteristics of the signal.This field is optional. In one embodiment a Transmitter Location field409 is used to characterize the received signals. In another embodimentthis field is filled by the location server. The mobile unit mayoptionally use this information to determine if it wants to capture thesignal emanating from a particular transmitter. Finally, Signal Strengthfields 411, 413, are filled by the mobile unit based on the signalstrengths of the received carriers. In one embodiment the signalstrength is sampled multiple times for each frequency in order to smoothout any variations. At least one of the Signal Strength fields isrequired to be filled by the mobile unit.

FIG. 10 shows the high level structure of the fingerprint database 501in one embodiment. As one skilled in the art can appreciate, there aremany methods for building, managing and searching databases. The purposeof FIG. 10 is merely to illustrate the structure of the database in oneembodiment. Each row in database 501 corresponds to one fingerprint. TheLat and Long fields indicate the latitude and longitude of the locationto which the fingerprint corresponds. In one instance the fingerprintcorresponds not to one exact spot on the surface of the earth, butinstead to a small area. The Lat and Long fields in this embodimentindicate a position inside the area, preferably the center point. TheTime column indicates the time at which the fingerprint was captured. Inone embodiment the UTC time format is used to indicate this time. TheFingerprint column contains the actual fingerprint data. In oneembodiment the structure depicted in FIG. 9 is used to store thefingerprint data. Finally, the Description column contains a shortdescription of the location corresponding to the fingerprint. Forexample, it may indicate a street address, or an intersection. Thisfield is optional.

In one embodiment, the database is built by taking off-line snapshots offingerprints at various locations. The fingerprint information alongwith the coordinates of the location are entered into the database. Themore the locations, the richer the database. The resolution of locationdetermination is also controlled by how far apart the fingerprintsamples are taken. The closer they are, the higher the resolution. Ofcourse, a person skilled in the art can appreciate that the resolutionof the database is limited by the sensitivity of the fingerprintmeasuring device. In one embodiment the fingerprints are taken usingvery sensitive signal measuring devices that enable locations that arevery close to each other to have distinct fingerprints.

In another embodiment, the database is built by taking fingerprintmeasurements at predetermined locations and using intelligent algorithmsthat interpolate the fingerprints at all locations in between thesampled locations. This method has the advantage of not requiring agreat many physical measurements to be made, however, it does sufferfrom some loss in accuracy. This is because, however clever, theinterpolating algorithms will not be as accurate as making the actualmeasurements.

In yet another embodiment, the database is generated on-line using smartalgorithms that can predict the fingerprints in a local area. Thisscheme is effective in instances where an approximate idea of the mobileunit location is already available. For example, this could be the cellin which the mobile unit is located.

Location estimation procedures using a predicted fingerprint model andfingerprint measurements from the mobile unit are subject to severalsources of error, including errors in measuring the fingerprints andmismatches between the fingerprint model and the real world. If thepredicted fingerprints in two locations are similar, either of theseerror sources may cause the measured fingerprint to more closely matchthe fingerprint model for the incorrect location than for the correctlocation. This situation leads to the possibility of fingerprintambiguities.

For this embodiment of the invention, when a fingerprint model isproduced for a particular region, it can be subjected to statisticalanalysis to assess both the location accuracy that may be expected inthat region and to identify specific areas that can have potentialambiguity problems. For each point in the fingerprint model, theprobability distribution of ambiguous locations may be calculated, as afunction of the level of errors expected, by computing the likelihoodthat each pair of location points will be confused at that error level.The characteristics of this distribution provide information about theaccuracy and confidence of location estimates. If the distribution at aparticular point is tight (low statistical variance), location estimatesof mobile unit's at that location should be quite accurate. If thedistribution at a particular point is very broad (high statisticalvariance), location estimates of mobile unit's at that location arelikely to be inaccurate. Conversely, if the calculated location estimateis at a point known to have high ambiguity (high statistical variance),the confidence attributed to that estimate should be low.

The following references are incorporated by reference in their entiretyfor all purposes:

-   1. T. Suzuki, et al., The Moving-body Position Detection Method;    Patent Application (Showa 63-195800), August 1988.-   2. M. Hellebrandt, et al., Estimating Position and Velocity of    Mobiles in a Cellular Radio Network, IEEE Trans. On Vehicular    Technology, Vol. 46, No. 1, pp. 65-71, February 1997.-   3. I. Gaspard, et al., Position Assignment in Digital Cellular    Mobile Radio Networks (e.g. GSM) Derived from Measurements at the    Protocol Interface, IEEE Journal, pp. 592-596, March 1997.-   4. J. Jimenez, et al., Mobile Location Using Coverage Information:    Theoretical Analysis and Results, COST 259 TD(98), April 1999.

Conclusion

In conclusion, it can be seen that this invention has one or more of thefollowing significant improvements over prior art for mobile unitlocation techniques: (1) it can be implemented without requiring anymodifications to the existing wireless network infrastructure, (2) onlyminor software changes in the mobile unit (typically a cellular phone)are required, and (3) it can be utilized in areas where GPS coverage iseither not available or not reliable. This follows because thefingerprints are generated by using portions of the RF spectrum thattypically have superior coverage and in-building penetration than GPSsignals.

While the above is a complete description of specific embodiments of theinvention, various modifications, alternative constructions, andequivalents may be used. Therefore, the above description should not betaken as limiting the scope of the invention as defined by the claims.

1. A method for estimating the location of a mobile unit, the methodcomprising: receiving the cell ID of the cell site servicing the mobileunit; generating a plurality of possible positions in which the mobileunit might be based on the cell ID; receiving an indication that themobile unit did not receive a first signal from a first transmitter;eliminating at least one of the plurality of possible positions for themobile unit as the estimate for the position of the mobile unit based onthe fact that the mobile unit did not receive the first signal from thefirst transmitter.
 2. The method of claim 1 wherein the indication fromthe mobile unit that the mobile unit does not receive the first signalfrom a first transmitter is inferred because the mobile unit failed toindicate that the mobile unit did receive the first signal.
 3. Themethod of claim 1 wherein the indication from the mobile unit that themobile unit does not receive the first signal from the first transmitteris explicit.
 4. The method of claim 1 further comprising: receiving anindication that the mobile unit does not receive a second signal from asecond transmitter; and eliminating at least one of the plurality ofpossible positions for the mobile unit as the estimate for the positionof the mobile unit based on the fact that the mobile unit did notreceive the second signal from the second transmitter.
 5. A methodcomprising: receiving an indication of a plurality of signals that amobile unit can receive and a plurality of signals that the mobile unitcannot receive; generating a plurality of possible positions for themobile unit based on the plurality of signals that the mobile unit canreceive and on the plurality of signals that the mobile unit cannotreceive; and choosing as an estimate of the position of the mobile unitone of the plurality of possible positions by comparing the fingerprintassociated with each of the plurality of possible positions against afingerprint received from the mobile unit.
 6. The method of claim 5wherein the indication from the mobile unit that the mobile unit doesnot receive the first signal from the first transmitter is inferredbecause the mobile unit failed to indicate that the mobile unit didreceive the first signal.
 7. The method of claim 5 wherein theindication from the mobile unit that the mobile unit does not receivethe first signal from the first transmitter is explicit.
 8. A methodcomprising: directing a mobile unit to report a signal strength valuefor the strongest of a first plurality of signals; receiving a signalstrength value from the mobile unit for a second plurality of signals,wherein the second plurality of signals is a non-empty subset of thefirst plurality of signals; and eliminating from consideration as thelocation of the mobile unit any location at which any of the secondplurality of signals is known to be less strong than any of the firstplurality of signals not reported.
 9. The method of claim 8 wherein arange of uncertainty is considered when determining when any of thesecond plurality of signals is compared with any of the first pluralityof signals.